@article {Mazarakis2007Vehicle,
	title = {Vehicle classification in Sensor Networks using time-domain signal processing and Neural Networks},
	journal = {Microprocessors and Microsystems},
	volume = {31},
	number = {6},
	year = {2007},
	month = {September},
	pages = {381{\textendash}392},
	abstract = {Vehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixed-size matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA{\textquoteright}s SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck.},
	keywords = {asr, classification, it, neuralnetworks},
	issn = {01419331},
	doi = {10.1016/j.micpro.2007.02.005},
	url = {http://dx.doi.org/10.1016/j.micpro.2007.02.005},
	author = {Mazarakis, G. and Avaritsiotis, J.}
}
@article {Zeng2005Development,
	title = {Development of a noise sources classification system based on new method for feature selection},
	journal = {Applied Acoustics},
	volume = {66},
	number = {10},
	year = {2005},
	month = {October},
	pages = {1196{\textendash}1205},
	abstract = {Feature selection of noise sources is important for noise sources detection and classification. In this paper, a new rough set based feature selection method has been given. Based on the method, a noise sources automatic classification system (NSACS) has been designed and validated. The key idea of the method is that most effective features can distinguish the most number of samples belonging to different classes of noise sources, if they are used for classification. This new approach has been applied into the system NSACS to select relevant features for artificial datasets and real-world datasets and the results have shown that this approach can correctly select all the relevant features of artificial datasets and at the same time it can drastically reduce the number of features. From the experiments, it can be found that to consider all the five datasets, the number of classification features after selection drops to 35\% and the accurate classification rate increases about 14\%. For the underwater noise sources dataset the number of features drops to 1/5 and the accurate classification rate increases about 6\% after feature selection.},
	keywords = {asr, bioacoustics, classification, it},
	issn = {0003682X},
	doi = {10.1016/j.apacoust.2005.02.007},
	url = {http://dx.doi.org/10.1016/j.apacoust.2005.02.007},
	author = {Zeng, X. and Zhan, Y.}
}
@article {Cowling2003Comparison,
	title = {Comparison of techniques for environmental sound recognition},
	journal = {Pattern Recognition Letters},
	volume = {24},
	number = {15},
	year = {2003},
	month = {November},
	pages = {2895{\textendash}2907},
	abstract = {This paper presents a comprehensive comparative study of artificial neural networks, learning vector quantization and dynamic time warping classification techniques combined with stationary/non-stationary feature extraction for environmental sound recognition. Results show 70\% recognition using mel frequency cepstral coefficients or continuous wavelet transform with dynamic time warping.},
	keywords = {asr, environment, it, recognition, soundscapes},
	issn = {01678655},
	doi = {10.1016/S0167-8655(03)00147-8},
	url = {http://dx.doi.org/10.1016/S0167-8655(03)00147-8},
	author = {Cowling, M.}
}
